Staff Software Engineer, Agent Platform

As a Staff Software Engineer on the Agent Platform team, you will design and build the systems that govern how Decagon agents operate in real-world environments.

You will own complex, distributed systems that sit at the heart of the agent runtime: execution frameworks, model orchestration logic, and experimentation platforms that ensure agents are fast, reliable, and continuously improving. Your work will directly impact how agents reason, take actions, and deliver outcomes across millions of interactions.

This role operates in a fast-moving, ambiguous space with tight feedback loops. You’ll move fluidly between diagnosing production issues, designing new system abstractions, and running experiments to improve agent behavior. You’ll collaborate closely with Research, Infra, and Product teams to ship improvements safely and at scale.

In this role, you will

Design and evolve agent harnesses that power different product experiences

Build core runtime systems, including AOP execution and multi-model orchestration

Develop control-plane logic for routing, planning, and tool invocation with strong safety guarantees

Optimize agent systems for latency, reliability, and production correctness

Analyze real-world failures and use data to drive iterative improvements

Build and operate online experimentation (A/B testing) and contribute to offline evaluation frameworks

Improve observability, testing, and simulation systems to ensure safe, measurable progress

Contribute to voice and real-time systems (e.g., transcription pipelines, turn-taking, latency improvements)

Continuously adapt orchestration systems as model capabilities evolve

Your background looks something like this

Strong experience building distributed systems or backend platforms in production environments

Comfort working in ambiguous, fast-moving environments with rapid iteration cycles

Experience owning systems end-to-end, from design through production and iteration

Familiarity with experimentation, evaluation, or data-driven product improvement loops

A track record of improving system reliability, performance, and observability

Ability to debug complex systems and identify root causes of failures

Even better

You’ve built or worked on agent harnesses, orchestration layers, or execution frameworks

You think in terms of control planes, feedback loops, and system-level optimization, not just features

You’re excited about diagnosing failure modes and iterating toward measurable improvements

You care deeply about production quality—not just making systems work, but making them reliable, safe, and scalable

You’re motivated by pushing the frontier of how intelligent systems behave in the real world

Compensation

$200K – $400K + Offers Equity

This range reflects the expected compensation for this role. Compensation within the range is determined based on experience, skills, and the scope of responsibilities, with flexibility for candidates who demonstrate exceptional impact.

In addition to base salary, we offer competitive equity. Final compensation may vary based on location within the United States.